In order to be truly competitive, finance professionals need to up their game when it comes to data analytics.
In today’s world, it would be hard to find any successful companies that would argue that data analytics doesn’t play an essential role in creating a company’s growth strategy. A recent article in the Journal of Accountancy, “The next frontier in data analytics” shows just how much of an emphasis CFOs place on having talent within that area.
Although the article places most of the emphasis on CPAs, it also cites a 2014 survey of 2,100 CFOs by staffing giant, Robert Half, which notes that 61% of respondents rate as either “mandatory for everyone” or “mandatory for some positions” business analytics skills, including business intelligence and data mining. According to the article’s authors, CFOs are increasing their use of predictive analytics, using the data to predict and achieve outcomes. That’s why an increasing number of finance executives are focusing on Big Data to identify patterns that companies can then use to drive overall strategy.
4 Types of Analytics
With the expansion of the global economy and the increased competition accompanying that expansion, it’s no longer enough to simply identify what or why something notable is occurring; now you need to use data to generate the insights needed for strategic decision-making. There are four different types of data analytics, from very basic to quite sophisticated.
Type 1 – Descriptive Analytics – Asks “what happened?” Most companies are familiar with and most likely use this type as a general summary of what is occurring within the business. Basic tools exist to assist in this type…think Google Analytics. Analysis in this area will tell you whether a campaign or product launch or other initiative was successful or not within a given period of time.
Type 2 – Diagnostic Analytics – Asks “why did it happen?” This type looks at the “what” and offers the “why” through drill-down, data discovery, and data mining. What you get from this deeper dive into the data is basic insights as to why your project did or didn’t meet its goals. It won’t get you where you need to go in today’s business environment.
Type 3 – Predictive Analytics – Asks “what will happen?” This type of data will provide actionable insights by using historical and transactional data, as well as data from a variety of sources, including CRM, ERP, and POS systems to identify patterns. Those patterns can then be used to predict customer behavior and create models for future initiatives.
Type 4 – Prescriptive Analytics – Asks “what should I do?” This is the most advanced of the four types of analytics. If you’ve been consistent in conducting predictive analytics, prescriptive analytics can help you identify the best options that will give you the optimal outcome or, just as important, give you the information necessary to prevent bad results.
For those companies that feel analytics aren’t really a major focus, the Journal of Accountancy article has a warning, “companies ignoring data analytics may be forced out of business in the long run.”
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